Elon Musk’s AI 'Gym'

Published April 28th at 11:00am

Artificial intelligence and specifically machine learning is well on it’s way to becoming one of the defining features of the technology of our time. The research currently being conducted has provided for some startling breakthroughs in AI, but just like any scientific area, good research is able to be replicated. If others can tweak your study and get more complete results then all the better - it’s ‘standing on the shoulders of giants’, to borrow a phrase from Isaac Newton.

This is the driving principle behind OpenAIGym, the brainchild of Elon Musk, Y Combinator's Sam Altman, and former Google Brain researcher Ilya Sutskever. The collaboration aims to conduct artificial intelligence research whilst publishing and open-sourcing almost everything they do. Such collaboration projects have existed before, albeit not necessarily being open source - but OpenAI stands aside from these for one very good reason.

OpenAIGym won't have leaderboards based on who can make the best algorithm to tackle a specific task - it will focus on promoting algorithms that can adapt to varying situations. This is known as ‘generalisation’ and is seen by researchers as the biggest hurdle to get over in creating a true, human like, AI. For example, Right now, we currently have algorithms that can correctly identify a building or a dog, yet can’t understand human speech because they approach the data in different ways. An algorithm that can generalise would be able to deal with these situations and more - just like a human.

The OpenAIGym focuses on learning through positive reinforcement, a type of machine learning that bears marked similarities to operant condition theory in psychology. If the AI does well, it gets a reward. If it fails, it gets nothing and then tries something different. This kind of reinforcement learning has proved to work extremely well with robots in a video game setting - Google Deepmind even used it to beat classic Atari games.

The project is up and running in Beta form right now and researchers are free to begin submitting their algorithms. The idea behind the open source nature is so that they can then see how their algorithm fared in testing, make adjustments, and publish their results - allowing anyone from the community to build upon their work. It’s almost like they’re crowdsourcing AI.

If you would like to read more about machine learning and artificial intelligence then why not read the blog series by David Di Domenico, Managing Director of IQ Analytics, below?